---
title: Databricks Unity Catalog (UC)
---

This notebook shows how to use UC functions as LangChain tools, with both LangChain and LangGraph agent APIs.

See Databricks documentation ([AWS](https://docs.databricks.com/en/sql/language-manual/sql-ref-syntax-ddl-create-sql-function.html)|[Azure](https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/sql-ref-syntax-ddl-create-sql-function)|[GCP](https://docs.gcp.databricks.com/en/sql/language-manual/sql-ref-syntax-ddl-create-sql-function.html)) to learn how to create SQL or Python functions in UC. Do not skip function and parameter comments, which are critical for LLMs to call functions properly.

In this example notebook, we create a simple Python function that executes arbitrary code and use it as a LangChain tool:

```sql
CREATE FUNCTION main.tools.python_exec (
  code STRING COMMENT 'Python code to execute. Remember to print the final result to stdout.'
)
RETURNS STRING
LANGUAGE PYTHON
COMMENT 'Executes Python code and returns its stdout.'
AS $$
  import sys
  from io import StringIO
  stdout = StringIO()
  sys.stdout = stdout
  exec(code)
  return stdout.getvalue()
$$
```

It runs in a secure and isolated environment within a Databricks SQL warehouse.

```python
%pip install -qU databricks-sdk langchain-community databricks-langchain langgraph mlflow
```

```output
Note: you may need to restart the kernel to use updated packages.
```

```python
from databricks_langchain import ChatDatabricks

llm = ChatDatabricks(endpoint="databricks-meta-llama-3-70b-instruct")
```

```python
from databricks_langchain.uc_ai import (
    DatabricksFunctionClient,
    UCFunctionToolkit,
    set_uc_function_client,
)

client = DatabricksFunctionClient()
set_uc_function_client(client)

tools = UCFunctionToolkit(
    # Include functions as tools using their qualified names.
    # You can use "{catalog_name}.{schema_name}.*" to get all functions in a schema.
    function_names=["main.tools.python_exec"]
).tools
```

(Optional) To increase the retry time for getting a function execution response, set environment variable UC_TOOL_CLIENT_EXECUTION_TIMEOUT. Default retry time value is 120s.

## LangGraph agent example

```python
import os

os.environ["UC_TOOL_CLIENT_EXECUTION_TIMEOUT"] = "200"
```

## LangGraph agent example

```python
from langchain.agents import create_agent

agent = create_agent(
    llm,
    tools,
    prompt="You are a helpful assistant. Make sure to use tool for information.",
)
agent.invoke({"messages": [{"role": "user", "content": "36939 * 8922.4"}]})
```

```output
{'messages': [HumanMessage(content='36939 * 8922.4', additional_kwargs={}, response_metadata={}, id='1a10b10b-8e37-48c7-97a1-cac5006228d5'),
  AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_a8f3986f-4b91-40a3-8d6d-39f431dab69b', 'type': 'function', 'function': {'name': 'main__tools__python_exec', 'arguments': '{"code": "print(36939 * 8922.4)"}'}}]}, response_metadata={'prompt_tokens': 771, 'completion_tokens': 29, 'total_tokens': 800}, id='run-865c3613-20ba-4e80-afc8-fde1cfb26e5a-0', tool_calls=[{'name': 'main__tools__python_exec', 'args': {'code': 'print(36939 * 8922.4)'}, 'id': 'call_a8f3986f-4b91-40a3-8d6d-39f431dab69b', 'type': 'tool_call'}]),
  ToolMessage(content='{"format": "SCALAR", "value": "329584533.59999996\\n", "truncated": false}', name='main__tools__python_exec', id='8b63d4c8-1a3d-46a5-a719-393b2ef36770', tool_call_id='call_a8f3986f-4b91-40a3-8d6d-39f431dab69b'),
  AIMessage(content='The result of the multiplication is:\n\n329584533.59999996', additional_kwargs={}, response_metadata={'prompt_tokens': 846, 'completion_tokens': 22, 'total_tokens': 868}, id='run-22772404-611b-46e4-9956-b85e4a385f0f-0')]}
```

## LangChain agent example

```python
from langchain.agents import AgentExecutor, create_tool_calling_agent
from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            "You are a helpful assistant. Make sure to use tool for information.",
        ),
        ("placeholder", "{chat_history}"),
        ("human", "{input}"),
        ("placeholder", "{agent_scratchpad}"),
    ]
)

agent = create_tool_calling_agent(llm, tools, prompt)
```

```python
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
agent_executor.invoke({"input": "36939 * 8922.4"})
```

```output
> Entering new AgentExecutor chain...

Invoking: `main__tools__python_exec` with `{'code': 'print(36939 * 8922.4)'}`


{"format": "SCALAR", "value": "329584533.59999996\n", "truncated": false}The result of the multiplication is:

329584533.59999996

> Finished chain.
```

```output
{'input': '36939 * 8922.4',
 'output': 'The result of the multiplication is:\n\n329584533.59999996'}
```
